Sensitivity Studies of Resistivity Monitoring for Shallow Enhanced Recovery Processes- A Numerical Case History
Bibliographic record
Abstract
Abstract Enhanced Oil Recovery (EOR) processes have been monitored using gas analysis, geophysical logging, observation wells (pressure and temperature), well testing methods, seismic velocity tomography, and inversion of deformation measurements. Each of these methods has various strengths and weaknesses. Most EOR processes involve large changes in the formation resistivity because of the introduction of a fluid phase with a different electrical conductivity, or because of a large temperature change. These massive changes in subsurface conductivity may be monitored remotely using subsurface-to-surface resistivity techniques to investigate propagation of fluid fronts and growth of affected zones with time. In this paper, we present general requirements for resistivity techniques in EOR monitoring, typical electrode installations to reduce noise and seasonal variations, and approaches to the inversion of three-dimensional (3D) EOR resistivity problems. Sensitivity analyses for a real reservoir case subjected to EOR are presented, and a method of rapid design evaluation for resistivity monitoring is outlined. It appears that that the analysis tools and the technology is now adequate to systematically develop electrical monitoring for various EOR applications. Introduction Geophysical techniques have been used for reservoir delineation and description for several decades. Currently, attempts are being made to modify geophysical techniques to actively monitor secondary and tertiary recovery processes such as water injection, cyclic steam stimulation, and in situ combustion. In principle, changes in geophysical responses with time can be used to estimate the lateral extent and volume of the reservoir affected by the EOR process. Thus, the main objective of EOR monitoring is to provide reliable spatial and temporal descriptions of fluid or temperature fronts. These data may be supplemented with production and observation well histories to estimate recovery efficiency and to develop production strategies. In practice, high-quality remote geophysical data should lead to improved process control and greater recovery ratios because they represent independent measures of recovery behavior which can be used to guide production strategies. Case histories of reservoir monitoring based on 3D/4D reflection seismology (Greaves and Fulp, 1987; Pullin et al., 1987; King et al., 1988; Eastwood et al., 1994; Mathisen et al., 1995a and 1995b, Lumley, 1995, and many more), crosshole seismic transmission (Justice et al., 1989; Bregman et al., 1989), surface deformation inversion (Dusseault et al., 1993) and electrical methods (Dorfman et al., 1977; Bartel, 1982; and many more) have been reported in the literature. These studies demonstrate the feasibility of geophysically monitoring EOR processes. With technological advances and innovative techniques, one may expect that it will become possible to more reliably monitor chemical flood evolution, gas distribution changes, and hydraulically induced fracture propagation. The most widely used and successful geophysical technique for describing reservoir processes is surface 3D/4D reflection seismics. Compared to surface seismics, crosshole methods or vertical seismic profiling (VSP) have greater vertical resolution. They are important aids in calibrating surface seismic survey for use in lateral variation studies of seismic velocity, but are invariably more costly than surface methods. Surface, VSP and crosshole methods are based on ray-path tracing and analysis of velocity changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".